Smartphone addiction and associated factors among postgraduate students in an Arabic sample: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Smartphone addiction, as with other behavioral addictions, is associated with social, physical, and mental health issues. In this article, we investigated the prevalence of smartphone addiction among postgraduate students and evaluated its correlation with social demographics, depression, attention-deficit/hyperactivity disorder (ADHD), and nicotine dependence. OBJECTIVES: The objective of this study was to investigate the prevalence of smartphone addiction among Middle Eastern postgraduate students, determine the factors associated with smartphone addiction, and estimate the incidence rate of major depressive disorder (MDD), ADHD, insomnia, and nicotine addiction among postgraduate students with smartphone addiction. METHODS: As part of a cross-sectional online survey, participants were given a self-questionnaire divided into six sections: Socio-demographics, Smartphone Addiction Scale (SAS), Patient Health Questionnaire (PHQ9) for Depression, Athens Insomnia Scale (AIS), the Fagerström Test for Cigarette Dependence Questionnaire (FTCd), and the adult ADHD Self-Report Scale (ASRS-v1.1). RESULTS: Of the 506 patients, 51.0% of the participants demonstrated smartphone addiction. A significant association was also observed between extensive smartphone use and MDD (P = 0.001). Of the smokers in this study, 41.5% were addicted to smartphones (P = 0.039). Smartphone addicts had approximately two times the chance of having insomnia (OR = 2.113) (P = 0.013). In addition, they showcased more ADHD symptoms (OR = 2.712) (P < 0.001). CONCLUSIONS: We found a positive association among insomnia, depression, adult ADHD, and smartphone addiction, which confirms the findings reported in the previous studies. Therefore, we encourage the scientific community to further study the impacts of smartphone addiction on the mental health of postgraduate students.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".